End‑to‑End CI/CD for the OpenClaw Full‑Stack Template

1. Introduction

The OpenClaw Full‑Stack Template provides a ready‑made React front‑end, Node.js back‑end, and PostgreSQL database, all pre‑wired for rapid AI‑agent development. When you’re building self‑hosted AI assistants, a reliable CI/CD pipeline is not a luxury—it’s a necessity to keep deployments fast, repeatable, and error‑free.

In today’s AI‑agent hype, teams are racing to ship features like ChatGPT and Telegram integration or OpenAI ChatGPT integration. Without automated pipelines, a single misconfiguration can break an entire fleet of assistants that users rely on 24/7.

Below you’ll find a step‑by‑step guide that walks you through the entire lifecycle—from code commit to live production on the UBOS homepage—using modern DevOps tools.

2. Prerequisites

• GitHub account with repository access.
• Docker Engine (≥ 20.10) installed locally.
• UBOS account – sign up on the UBOS partner program if you need enterprise support.
• Node.js (≥ 18) and npm for local testing.

Clone the OpenClaw template repository and create a new branch for your feature work:

git clone https://github.com/ubos/openclaw-fullstack-template.git
cd openclaw-fullstack-template
git checkout -b feature/awesome-agent

Make sure you have the Enterprise AI platform by UBOS enabled for your organization to access private Docker registries and secure environment variables.

3. Setting up GitHub Actions

GitHub Actions orchestrates the CI/CD flow. Create a workflow file at .github/workflows/ci-cd.yml with the following structure:

name: CI/CD Pipeline

on:
  push:
    branches: [ main, feature/* ]
  pull_request:
    branches: [ main ]

jobs:
  build-test-deploy:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout repository
        uses: actions/checkout@v3

      - name: Set up Node.js
        uses: actions/setup-node@v3
        with:
          node-version: '18'

      - name: Install dependencies
        run: npm ci

      - name: Run unit tests
        run: npm test

      - name: Build Docker image
        run: |
          docker build -t ghcr.io/${{ github.repository }}:${{ github.sha }} .
          docker push ghcr.io/${{ github.repository }}:${{ github.sha }}

      - name: Deploy to UBOS
        env:
          UBOS_API_KEY: ${{ secrets.UBOS_API_KEY }}
        run: |
          curl -X POST https://api.ubos.tech/v1/deploy \\
            -H "Authorization: Bearer $UBOS_API_KEY" \\
            -d '{"image":"ghcr.io/${{ github.repository }}:${{ github.sha }}","app":"openclaw"}'

This workflow performs four core actions:

1. Checks out the code.
2. Installs Node.js dependencies.
3. Runs the test suite.
4. Builds and pushes a Docker image, then triggers a deployment via the UBOS API.

Store your UBOS_API_KEY as a secret in the repository settings to keep credentials safe.

4. Docker Configuration

The OpenClaw template ships with a Dockerfile that uses a multi‑stage build to keep the final image lightweight.

# ---- Build Stage ----
FROM node:18-alpine AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci
COPY . .
RUN npm run build   # React front‑end compiled to /app/build

# ---- Production Stage ----
FROM node:18-alpine
WORKDIR /app
COPY --from=builder /app/build ./frontend
COPY --from=builder /app/server ./server
EXPOSE 3000
CMD ["node", "server/index.js"]

Key points:

• Builder image: Installs dev dependencies and compiles the React UI.
• Production image: Copies only the compiled assets and runtime server code, resulting in a sub‑100 MB container.

When the GitHub Actions workflow runs docker build, it automatically creates this optimized image, which is then pushed to GitHub Container Registry (GHCR) for UBOS to pull.

5. Automated Tests

Testing is the safety net that guarantees your AI‑agent behaves as expected after each change.

5.1 Unit Tests with Jest

OpenClaw uses Jest for unit testing. A typical test file looks like:

// tests/api/user.test.js
const request = require('supertest');
const app = require('../server/app');

describe('User API', () => {
  it('should create a new user', async () => {
    const res = await request(app)
      .post('/api/users')
      .send({ name: 'Alice', email: 'alice@example.com' });
    expect(res.statusCode).toBe(201);
    expect(res.body).toHaveProperty('id');
  });
});

5.2 Integration Tests for API Endpoints

Integration tests spin up the full stack using Docker Compose. Add a docker-compose.test.yml file:

version: '3.8'
services:
  db:
    image: postgres:15-alpine
    environment:
      POSTGRES_USER: test
      POSTGRES_PASSWORD: test
      POSTGRES_DB: openclaw_test
  api:
    build: .
    environment:
      DATABASE_URL: postgres://test:test@db:5432/openclaw_test
    depends_on:
      - db

Run the suite with:

docker compose -f docker-compose.test.yml up --abort-on-container-exit --exit-code-from api

Successful integration tests give you confidence that the entire stack—including database migrations—works before the image reaches production.

6. Deploying to UBOS

UBOS provides a CLI and a REST API for one‑click deployments. After the Docker image is pushed, the GitHub Actions step calls the API, but you can also trigger deployments manually.

6.1 Using the UBOS CLI

# Install the UBOS CLI (Node.js based)
npm i -g @ubos/cli

# Log in (API key stored locally)
ubos login --api-key $UBOS_API_KEY

# Deploy the latest image
ubos deploy openclaw \
  --image ghcr.io/your-org/openclaw-fullstack-template:${GITHUB_SHA}

6.2 One‑Click Deployment Script

For teams that prefer a single script, add deploy.sh to the repo:

#!/bin/bash
set -e

IMAGE="ghcr.io/${GITHUB_REPOSITORY}:${GITHUB_SHA}"
curl -X POST https://api.ubos.tech/v1/deploy \
  -H "Authorization: Bearer $UBOS_API_KEY" \
  -d "{\"image\":\"$IMAGE\",\"app\":\"openclaw\"}"
echo "✅ Deployment triggered for $IMAGE"

Make the script executable and call it from the workflow:

- name: Deploy to UBOS
  run: ./deploy.sh
  env:
    UBOS_API_KEY: ${{ secrets.UBOS_API_KEY }}

6.3 Post‑Deployment Verification

After deployment, verify the health endpoint and the AI‑agent’s basic functionality:

# Health check
curl -f https://openclaw.yourdomain.com/health || exit 1

# Simple AI request (example using the built‑in ChatGPT wrapper)
curl -X POST https://openclaw.yourdomain.com/api/chat \
  -H "Content-Type: application/json" \
  -d '{"prompt":"Hello, AI!"}'

If both commands succeed, your CI/CD pipeline is fully operational.

For a visual overview of the deployment process, visit the host OpenClaw on UBOS page, which includes a live demo and logs.

7. Internal Link Integration

Embedding relevant internal resources improves SEO and helps readers discover related capabilities. Below is an example of how you can naturally weave a link into the deployment narrative:

“Once the Docker image is live, you can manage scaling, environment variables, and logs directly from the Workflow automation studio, which provides a UI for monitoring CI/CD pipelines.”

This approach not only enriches the article but also signals to search engines the contextual relevance of UBOS’s broader ecosystem.

8. AI‑Agent Hype: Why Robust CI/CD Matters

In 2024, AI agents have moved from experimental chatbots to mission‑critical assistants that schedule meetings, generate code, and even run customer‑support operations. According to a recent industry report, enterprises deploying AI agents expect a 30 % reduction in operational overhead.

Such expectations raise the stakes for reliability. A broken deployment can halt an entire sales funnel or disrupt a 24/7 support line. By automating builds, tests, and rollouts with the pipeline described above, you eliminate human error, ensure consistent environments, and gain instant rollback capabilities—essential ingredients for scaling AI‑agent services safely.

UBOS’s AI marketing agents showcase how a well‑orchestrated CI/CD pipeline can power personalized campaigns without manual intervention, reinforcing the business case for automation.

9. Conclusion & Next Steps

We’ve covered the full lifecycle of CI/CD for the OpenClaw Full‑Stack Template:

1. Set up prerequisites and clone the repository.
2. Configure a GitHub Actions workflow that builds, tests, and pushes Docker images.
3. Use a multi‑stage Dockerfile to keep production images lean.
4. Write unit and integration tests to safeguard functionality.
5. Deploy automatically to UBOS via CLI or API, then verify health.

Ready to explore more?

• Browse the UBOS templates for quick start to accelerate new AI projects.
• Check out the UBOS portfolio examples for real‑world deployments.
• Leverage the AI SEO Analyzer to fine‑tune your site’s search visibility.
• Experiment with the AI Article Copywriter to generate documentation for future releases.

By integrating these tools, you’ll not only streamline your development workflow but also position your AI agents for sustainable growth in a rapidly evolving market.